##### **ERROR MESSAGE: Bad input: Error during negative model training. Minimum number of variants to use in training is larger than the whole call set. One can attempt to lower the --minNumBadVariants arugment but this is unsafe.**

Best Answer

If that was a typo when you copied to the forum post, then your resource file is probably okay.

However there are a couple of things that are problematic in your command line.

First, you're recalibrating both SNPs and indels in the same run. But if your SNPs and indels were called with UnifiedGenotyper, it is better to recalibrate them separately. This is due to how the calling models are made by the UG.

Edit: we have updated our recommendations; from now on you should always recalibrate SNPs and indels separately, regardless of how they were called.

Second (and more important) the error tells you that you don't have enough variants to use VQSR with the default settings. This is explained in the Best Practices document. You can try the suggested adjustments, but if that still gives you error messages, then you cannot use VQSR and you will have to do hard-filtering instead. The Best Practices document gives you some recommendations for doing that.

Sorry, it was my typo when I posted code. In my code, it was "resource:omni,"

After I get this error, I added
"--minNumBadVariants 1500"

I get the following errors:
"

ERROR MESSAGE: NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe. Please consider raising the number of variants used to train the negative model (via --percentBadVariants 0.05, for example) or lowering the maximum number of Gaussians to use in the model (via --maxGaussians 4, for example)

If that was a typo when you copied to the forum post, then your resource file is probably okay.

However there are a couple of things that are problematic in your command line.

First, you're recalibrating both SNPs and indels in the same run. But if your SNPs and indels were called with UnifiedGenotyper, it is better to recalibrate them separately. This is due to how the calling models are made by the UG.

Edit: we have updated our recommendations; from now on you should always recalibrate SNPs and indels separately, regardless of how they were called.

Second (and more important) the error tells you that you don't have enough variants to use VQSR with the default settings. This is explained in the Best Practices document. You can try the suggested adjustments, but if that still gives you error messages, then you cannot use VQSR and you will have to do hard-filtering instead. The Best Practices document gives you some recommendations for doing that.